<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Machine Learning on VONNG</title><link>https://blog.vonng.com/en/tags/machine-learning/</link><description>Recent content in Machine Learning on VONNG</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Mon, 13 Jul 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://blog.vonng.com/en/tags/machine-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Getting the Name Right: What Is a World Model?</title><link>https://blog.vonng.com/en/ai/world-model/</link><pubDate>Mon, 13 Jul 2026 00:00:00 +0000</pubDate><guid>https://blog.vonng.com/en/ai/world-model/</guid><description>Starting from the roots of &amp;ldquo;world&amp;rdquo; and &amp;ldquo;model,&amp;rdquo; this essay uses Pearl&amp;rsquo;s ladder of causation to redefine world models: they must capture not just space and time, but agents, interventions, and counterfactuals.</description></item><item><title>The Cerebellum: The Other Half of Intelligence—and the Strongest AI Hasn't Touched It</title><link>https://blog.vonng.com/en/ai/cerebellum/</link><pubDate>Wed, 10 Jun 2026 00:00:00 +0000</pubDate><guid>https://blog.vonng.com/en/ai/cerebellum/</guid><description>The cerebellum changed how I see AI&amp;rsquo;s frontier: LLMs have already absorbed humanity&amp;rsquo;s explicit knowledge and are beginning to acquire interventional data through agentic RL. What they still lack is a vessel for individual history.</description></item><item><title>KNN Ultimate Optimization: From RDS to PostGIS</title><link>https://blog.vonng.com/en/pg/knn-optimize/</link><pubDate>Wed, 06 Jun 2018 00:00:00 +0000</pubDate><guid>https://blog.vonng.com/en/pg/knn-optimize/</guid><description>Ultimate optimization of KNN problems, from traditional relational design to PostGIS</description></item><item><title>Building an ItemCF Recommender in Pure SQL</title><link>https://blog.vonng.com/en/pg/pg-recsys/</link><pubDate>Wed, 05 Apr 2017 00:00:00 +0000</pubDate><guid>https://blog.vonng.com/en/pg/pg-recsys/</guid><description>Five minutes, PostgreSQL, and the MovieLens dataset—that’s all you need to implement a classic item-based collaborative filtering recommender.</description></item></channel></rss>